Using isin() to determine what should be printed

Question

Right now I have two dataframes (data1 and data2)

I would like to print a column of string values in the dataframe called data1, based on w

Answer 1

If all values of ids are unique:

I think you need merge with inner join. For data2 select only id column, on parameter should be omit, because joining on all columns - here only id:

df = pd.merge(data1, data2[['id']])

Sample:

data1 = pd.DataFrame({'id':list('abcdef'),
                      'B':[4,5,4,5,5,4],
                      'C':[7,8,9,4,2,3]})

print (data1)
   B  C id
0  4  7  a
1  5  8  b
2  4  9  c
3  5  4  d
4  5  2  e
5  4  3  f

data2 = pd.DataFrame({'id':list('frcdeg'),
                      'D':[1,3,5,7,1,0],
                      'E':[5,3,6,9,2,4],})

print (data2)
   D  E id
0  1  5  f
1  3  3  r
2  5  6  c
3  7  9  d
4  1  2  e
5  0  4  g

df = pd.merge(data1, data2[['id']])
print (df)
   B  C id
0  4  9  c
1  5  4  d
2  5  2  e
3  4  3  f

If id are duplicated in one or another Dataframe use another answer, also added similar solutions:

df = data1[data1['id'].isin(set(data1['id']) & set(data2['id']))]

---

ids = set(data1['id']) & set(data2['id'])
df = data2.query('id in @ids')

---

df = data1[np.in1d(data1['id'], np.intersect1d(data1['id'], data2['id']))]

Sample:

data1 = pd.DataFrame({'id':list('abcdef'),
                      'B':[4,5,4,5,5,4],
                      'C':[7,8,9,4,2,3]})

print (data1)
   B  C id
0  4  7  a
1  5  8  b
2  4  9  c
3  5  4  d
4  5  2  e
5  4  3  f

data2 = pd.DataFrame({'id':list('fecdef'),
                      'D':[1,3,5,7,1,0],
                      'E':[5,3,6,9,2,4],})

print (data2)
   D  E id
0  1  5  f
1  3  3  e
2  5  6  c
3  7  9  d
4  1  2  e
5  0  4  f

df = data1[data1['id'].isin(set(data1['id']) & set(data2['id']))]
print (df)
   B  C id
2  4  9  c
3  5  4  d
4  5  2  e
5  4  3  f

EDIT:

You can use:

df = data2.loc[data1['id'].isin(set(data1['id']) & set(data2['id'])), ['title']]

ids = set(data1['id']) & set(data2['id'])
df = data2.query('id in @ids')[['title']]

df = data2.loc[np.in1d(data1['id'], np.intersect1d(data1['id'], data2['id'])), ['title']]

Answer 2

You can compute the set intersection of the two columns -

ids = set(data1['id']).intersection(data2['id'])

Or,

ids = np.intersect1d(data1['id'], data2['id'])

Next, query/filter out relevant rows.

data1.loc[data1['id'].isin(ids), 'id']